A racially unbiased mammogram analyzer includes an interface for receiving mammograms; a processor for extracting features of mammograms of general population; a processor for extracting features of mammograms of a specific race. In one embodiment, the general population mammogram features are represented by middle layers of a CNN and the race specific features are represented by the end layer of the CNN network. In one embodiment, the race specific layers of CNN change dynamically according to the race indication done explicitly. In one embodiment the race specific layers of CNN change dynamically according to the race indication given by race indication processor. In one embodiment, the race indications are computed by a network of parallel variational autoencoder networks. In one embodiment, the race indicator computes race specific information to the CNN and are provided by variational autoencoders.
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2. The system of claim 1 wherein the first weightings correspond to a cancer diagnosis.
The system relates to medical diagnostics, specifically a method for analyzing biological data to improve disease detection, with a focus on cancer diagnosis. The system processes biological data, such as genetic or molecular information, to generate diagnostic predictions. It applies a set of weightings to the data, where these weightings are specifically calibrated to identify cancer-related patterns. The system may also include a training mechanism that adjusts these weightings based on reference data, ensuring accurate and reliable cancer diagnosis. Additionally, the system may incorporate multiple weighting schemes, allowing it to adapt to different types of cancer or patient-specific factors. The overall goal is to enhance diagnostic accuracy by leveraging optimized weightings tailored to cancer detection, reducing false positives and improving early diagnosis. The system may be integrated into clinical workflows, providing healthcare professionals with actionable insights for patient care.
3. The system of claim 1 wherein the input to the convolutional neural network does not include an identification of an ethnicity of the breast image.
This invention relates to a computer-implemented system for analyzing breast images using a convolutional neural network (CNN) to detect abnormalities, such as tumors or lesions, without relying on ethnicity-related input data. The system processes breast images, such as mammograms or ultrasound scans, through a CNN trained to identify medical conditions based solely on visual features of the breast tissue. The CNN is designed to avoid using ethnicity as an input parameter, ensuring unbiased analysis and reducing potential biases in diagnostic outcomes. The system may include preprocessing steps to standardize image quality and enhance relevant features before feeding them into the CNN. The network's architecture may include multiple convolutional layers to extract hierarchical patterns, followed by fully connected layers for classification. The system may also incorporate post-processing techniques to refine detection results and improve accuracy. By excluding ethnicity from the input, the system aims to provide fair and consistent diagnostic performance across diverse patient populations. The invention addresses the need for unbiased medical imaging analysis, particularly in breast cancer screening, where demographic factors should not influence diagnostic accuracy. The system may be integrated into clinical workflows to assist radiologists in early detection and treatment planning.
5. The system of claim 4 wherein when the ethnicity of the breast image for analysis is first ethnicity, the first weightings for the first different features are not input to the third layer.
This invention relates to a machine learning-based system for analyzing breast images, particularly focusing on improving accuracy by adjusting feature weightings based on patient ethnicity. The system addresses the problem of biased or inaccurate breast cancer detection in machine learning models when trained on diverse populations, as certain features may have different diagnostic significance across ethnic groups. The system includes a neural network with multiple layers, where the first layer extracts features from the breast image, the second layer applies weightings to these features, and the third layer processes the weighted features to generate an analysis output. The system dynamically adjusts the weightings applied in the second layer based on the detected ethnicity of the patient. If the breast image belongs to a first ethnicity, the system bypasses the first set of weightings for the first set of features, preventing them from being input to the third layer. This ensures that only relevant features are considered for analysis, improving diagnostic accuracy. The system may also include a preprocessing module to enhance image quality and a feature extraction module to identify key diagnostic features. The ethnicity detection may be performed using demographic data or image-based analysis. The overall goal is to reduce bias and enhance the reliability of breast cancer detection across different ethnic groups.
6. The system of claim 4 wherein when the ethnicity of the breast image for analysis is second ethnicity, the first weightings for the first different features are not input to the second layer.
The system relates to medical imaging analysis, specifically for breast cancer detection using machine learning models. The problem addressed is the variability in breast tissue characteristics across different ethnic groups, which can lead to reduced accuracy in automated diagnostic systems if not properly accounted for. The system improves upon prior art by dynamically adjusting feature weightings based on the ethnicity of the patient to enhance diagnostic accuracy. The system includes a machine learning model with multiple layers, where the first layer processes input breast images and extracts features. These features are then weighted differently depending on the patient's ethnicity. For a first ethnicity, the system applies a set of first weightings to specific features before passing them to a second layer for further analysis. However, when the ethnicity of the breast image is a second ethnicity, the system does not apply these first weightings to the first set of features, instead using alternative weightings or omitting them entirely. This ensures that the model adapts to ethnic-specific variations in breast tissue, improving diagnostic performance across diverse populations. The system may also include preprocessing steps to determine ethnicity from the image or metadata, ensuring the correct weightings are applied. The overall goal is to reduce false positives and negatives by tailoring feature importance to the patient's demographic background.
8. The system of claim 1 wherein each of the first layers, second layer, and third layer has a single vote, and a diagnosis probability of breast cancer is based on a majority of the votes such that any two of the first layers, second layer, and third layer would constitute a majority.
This invention relates to a diagnostic system for breast cancer detection using a multi-layered voting mechanism. The system addresses the challenge of improving diagnostic accuracy by reducing reliance on a single decision-making layer, which can be prone to errors due to variability in data interpretation or model biases. The system comprises three distinct layers: a first layer, a second layer, and a third layer. Each layer independently evaluates input data, such as medical imaging or patient records, to generate a diagnostic vote. The first layer may use a machine learning model trained on specific features, while the second layer could employ a different algorithm or dataset. The third layer may incorporate expert rules or additional data sources. Each layer contributes a single vote—either positive or negative—for breast cancer diagnosis. The final diagnosis probability is determined by a majority vote, meaning any two of the three layers must agree to reach a consensus. This design ensures robustness, as no single layer can unilaterally influence the outcome, reducing the risk of false positives or negatives. The system is particularly useful in clinical settings where diagnostic accuracy is critical.
9. The system of claim 1 wherein the multiple ethnicities of the first layers include the first and second ethnicities.
A system for analyzing and processing facial recognition data addresses challenges in accurately identifying and categorizing individuals across diverse ethnic backgrounds. The system includes multiple layers of facial recognition models, each trained to specialize in recognizing distinct ethnic groups. The first layers of the system are designed to handle multiple ethnicities, specifically including at least two distinct ethnicities, such as the first and second ethnicities. These layers improve recognition accuracy by focusing on specific ethnic features, reducing bias and errors that can occur with generalized models. The system may also include additional layers that further refine recognition based on other ethnic or demographic factors. By structuring the recognition process in this layered approach, the system enhances overall performance, particularly in applications requiring high accuracy across diverse populations, such as security, law enforcement, or personalized user experiences. The system may integrate with existing facial recognition databases or operate independently to provide more reliable and inclusive identification results.
10. The system of claim 1 wherein the multiple ethnicities of the first layers does not include at least one of the first ethnicity or second ethnicity.
This invention relates to a system for analyzing or processing data involving multiple ethnicities, particularly in layered data structures. The problem addressed is the need to exclude specific ethnicities from certain layers of the system while retaining others, ensuring flexibility in data handling. The system includes multiple layers, each associated with different ethnicities. The first layers of the system are configured such that they do not include at least one of the first or second ethnicity. This exclusion allows for targeted data processing, filtering, or analysis where certain ethnicities must be omitted from specific layers. The remaining layers may include these excluded ethnicities or other ethnicities, depending on the system's design. The system may be used in applications such as demographic analysis, personalized content delivery, or bias mitigation in machine learning models. By selectively excluding ethnicities from certain layers, the system can adapt to regulatory requirements, user preferences, or ethical considerations. The exclusion mechanism ensures that data processing remains compliant with constraints while maintaining functionality for other ethnicities. The system may also include additional layers or components that further refine or expand the data handling capabilities.
11. The system of claim 1 wherein the first set of training data comprising first breast images do not comprise the third images or fourth images.
The system relates to medical imaging, specifically to training data management for breast image analysis. The problem addressed is ensuring that training datasets for machine learning models in breast imaging are properly segmented to avoid data leakage or contamination between different subsets of images. This is critical for accurate model performance and reliable diagnostic outcomes. The system includes a training data management module that organizes breast images into distinct sets. The first set of training data consists of first breast images, which are intentionally excluded from a third set and a fourth set of images. This segregation ensures that the first set remains independent for validation or testing purposes, preventing overlap with other datasets that may be used for training or fine-tuning the model. The third and fourth sets may contain different types of breast images, such as normal, abnormal, or augmented images, and their exclusion from the first set maintains data integrity. The system may also include preprocessing modules to standardize image formats, normalization techniques, and labeling systems to enhance dataset consistency. The overall goal is to improve the robustness and reliability of machine learning models in breast imaging applications by ensuring clean, non-overlapping datasets for training, validation, and testing.
17. The system of claim 1 wherein the first breast images comprises mammograms.
The system is designed for breast imaging analysis, specifically addressing the need for accurate and efficient evaluation of breast tissue to detect abnormalities such as tumors or lesions. The system captures and processes breast images, particularly mammograms, which are X-ray images of the breast used for early detection of breast cancer. Mammograms provide detailed views of the breast tissue, allowing for the identification of suspicious areas that may require further investigation. The system enhances the diagnostic process by analyzing these images to highlight potential abnormalities, reducing the reliance on manual interpretation and improving detection accuracy. By focusing on mammograms, the system leverages a widely used and standardized imaging modality, ensuring compatibility with existing medical practices. The analysis may include techniques such as image enhancement, segmentation, and pattern recognition to differentiate between normal and abnormal tissue structures. This automated approach helps radiologists prioritize cases, streamline workflows, and improve patient outcomes by enabling earlier and more precise diagnoses. The system integrates seamlessly into clinical environments, supporting both screening and diagnostic applications.
18. The system of claim 1 wherein the first set of training data comprises ethnicity data for a general population, the second set of training data comprises ethnicity data for a Hispanic ethnicity, the third set of training data comprises ethnicity data for a Chinese ethnicity, and the fourth set of training data comprises ethnicity data for an Indian ethnicity.
The system is designed to improve the accuracy of ethnicity classification in data analysis by using multiple sets of training data representing different ethnic groups. The system addresses the problem of biased or inaccurate ethnicity classification when using a single, generalized training dataset that may not adequately represent diverse populations. To solve this, the system employs a first set of training data containing ethnicity data for a general population, ensuring a broad baseline for comparison. Additionally, it includes specialized training datasets for specific ethnic groups: a second set for Hispanic ethnicity, a third set for Chinese ethnicity, and a fourth set for Indian ethnicity. These targeted datasets allow the system to refine its classification models for each group, reducing errors and biases that arise from relying solely on generalized data. By incorporating these distinct datasets, the system enhances the precision of ethnicity classification across different demographic groups, improving fairness and reliability in applications such as healthcare, marketing, and social research. The use of multiple, ethnicity-specific datasets ensures that the system can accurately distinguish between these groups, addressing the limitations of previous approaches that relied on a single, non-specific training dataset.
19. The system of claim 4 wherein each of the first layers, second layer, and third layer has a single vote, and a diagnosis probability of breast cancer is based on a majority of the votes such that any two of the first layers, second layer, and third layer would constitute a majority.
This invention relates to a diagnostic system for breast cancer detection using a multi-layered voting mechanism. The system addresses the challenge of improving diagnostic accuracy by reducing reliance on a single diagnostic layer, which may be prone to errors. The system includes three distinct diagnostic layers: a first set of layers, a second layer, and a third layer. Each layer independently evaluates input data to generate a diagnostic vote. The first set of layers may include multiple sub-layers, each contributing a single vote, while the second and third layers each contribute one vote. The final diagnosis probability is determined by a majority vote among the layers. Specifically, any two of the three layers (first set, second, or third) can form a majority, ensuring robustness against single-layer failures. This approach enhances diagnostic reliability by leveraging redundant evaluations, reducing the impact of individual layer inaccuracies. The system is designed to integrate with existing diagnostic workflows, providing a more dependable breast cancer assessment.
20. The system of claim 4 wherein the multiple ethnicities of the first layers include the first and second ethnicities.
The system relates to a multi-layered facial recognition or analysis system designed to improve accuracy across diverse ethnic groups. The core problem addressed is the bias in traditional facial recognition systems, which often perform poorly when applied to individuals from underrepresented ethnicities. The system includes multiple layers of facial recognition models, each optimized for different ethnic groups. The first layers of the system are specifically trained to recognize facial features from at least two distinct ethnicities, ensuring that the initial processing stages account for variations in skin tone, facial structure, and other ethnicity-specific characteristics. Subsequent layers may further refine recognition or classification tasks. By incorporating ethnicity-specific models early in the processing pipeline, the system aims to reduce errors and improve reliability when identifying or analyzing faces from diverse populations. The approach contrasts with homogeneous systems that apply a single model to all ethnicities, which often leads to higher error rates for minority groups. The system may be used in applications such as security, biometric authentication, or demographic analysis, where accurate and unbiased facial recognition is critical.
21. The system of claim 4 wherein the multiple ethnicities of the first layers does not include at least one of the first ethnicity or second ethnicity.
This invention relates to a system for analyzing or processing data involving multiple ethnicities, particularly in layered or hierarchical structures. The system addresses the challenge of ensuring diversity and representation in data analysis by controlling the inclusion or exclusion of specific ethnicities in different layers of the system. The system includes multiple layers, where each layer is associated with one or more ethnicities. The key feature is that at least one of the first layers in the system does not include at least one of the first ethnicity or the second ethnicity. This exclusion can be used to prevent bias, ensure fairness, or enforce specific representation criteria in data processing tasks. The system may be used in applications such as machine learning, demographic analysis, or personalized services where ethnicity-based segmentation is required. The exclusion mechanism allows for flexible configuration of ethnic representation across different layers, enabling tailored solutions for diverse use cases. The system may also include additional layers with different ethnicity configurations to further refine data processing outcomes.
22. The system of claim 4 wherein the first set of training data comprising first breast images do not comprise the third images or fourth images.
The invention relates to a medical imaging system for breast cancer detection, specifically addressing the challenge of training machine learning models with diverse and representative datasets. The system uses a training dataset that includes breast images from different sources or modalities, ensuring that the model is exposed to a wide range of imaging variations. The key innovation is that the first set of training data, which consists of breast images, does not include certain other types of images (referred to as third and fourth images) that may introduce bias or noise into the training process. This exclusion helps improve the accuracy and reliability of the model by preventing contamination from irrelevant or low-quality data. The system may also incorporate additional sets of training data, each with specific characteristics, to further enhance the model's performance. The overall goal is to develop a robust machine learning model capable of accurately detecting breast cancer from medical images while minimizing errors caused by dataset inconsistencies.
23. The system of claim 4 wherein the first breast images comprises mammograms.
The invention relates to a medical imaging system designed to improve breast cancer detection and diagnosis. The system captures and processes breast images, particularly mammograms, to enhance the accuracy and efficiency of screening and diagnostic procedures. Mammograms are X-ray images of the breast used to detect abnormalities such as tumors, cysts, or other lesions. The system may include imaging devices, image processing algorithms, and analysis tools to analyze the mammographic data. These tools may identify and highlight suspicious areas, compare images over time to detect changes, and assist radiologists in making more informed decisions. The system may also integrate with other diagnostic modalities or patient records to provide a comprehensive assessment. By leveraging advanced imaging techniques and computational analysis, the system aims to reduce false positives, improve early detection rates, and streamline workflows in clinical settings. The focus on mammograms ensures compatibility with widely used breast imaging standards, making the system adaptable to existing healthcare infrastructures.
24. The system of claim 4 wherein the first set of training data comprises ethnicity data for a general population, the second set of training data comprises ethnicity data for a Hispanic ethnicity, the third set of training data comprises ethnicity data for a Chinese ethnicity, and the fourth set of training data comprises ethnicity data for an Indian ethnicity.
The invention relates to a system for analyzing and processing ethnicity data to improve accuracy in demographic classification. The system addresses the problem of biased or inaccurate demographic data analysis by using multiple sets of training data representing different ethnic groups. The system includes a data processing module that receives and processes ethnicity data from various sources. The first set of training data includes ethnicity data for a general population, while the second, third, and fourth sets of training data specifically include ethnicity data for Hispanic, Chinese, and Indian ethnicities, respectively. By using these distinct training datasets, the system can more accurately classify and analyze demographic information, reducing errors and biases that may arise from relying on a single, generalized dataset. The system may also include a machine learning model trained on these datasets to enhance classification performance. The use of multiple, ethnicity-specific datasets ensures that the system can adapt to the unique characteristics of each group, improving overall accuracy and reliability in demographic analysis.
25. The system of claim 12 wherein each of the first layers, second layer, and third layer has a single vote, and a diagnosis probability of breast cancer is based on a majority of the votes such that any two of the first layers, second layer, and third layer would constitute a majority.
This invention relates to a diagnostic system for breast cancer detection using a multi-layered voting mechanism. The system addresses the challenge of improving diagnostic accuracy by reducing reliance on a single diagnostic layer, which may be prone to errors or biases. The system includes three distinct diagnostic layers: a first set of layers, a second layer, and a third layer. Each layer independently evaluates input data to generate a diagnostic vote. The first layers may include multiple sub-layers, each contributing a single vote, while the second and third layers each contribute one vote. The final diagnosis probability is determined by a majority vote among the three layers. A majority is achieved if any two of the three layers agree, ensuring robustness against individual layer errors. This design enhances reliability by requiring consensus, reducing the impact of false positives or negatives from any single diagnostic source. The system is particularly useful in medical imaging or data analysis where multiple diagnostic approaches are available, and cross-validation improves confidence in the final result.
26. The system of claim 12 wherein the multiple ethnicities of the first layers include the first and second ethnicities.
The system relates to a multi-layered data processing framework designed to analyze and categorize information based on ethnic diversity. The core problem addressed is the need for accurate and scalable classification of data across different ethnic groups, particularly in applications like demographic analysis, marketing, or social research. The system includes multiple layers of data processing, where each layer is configured to handle specific ethnic groups. The first layers of the system are specifically tailored to process data associated with at least two distinct ethnicities, ensuring that the system can accurately distinguish and analyze information from these groups. The system may also include additional layers for other ethnicities, allowing for comprehensive and inclusive data processing. The layers work in tandem to improve the accuracy and efficiency of ethnic-based data classification, reducing errors and biases that may arise from generalized or non-specific processing methods. This layered approach enables the system to adapt to diverse datasets while maintaining high precision in ethnic categorization. The system may be integrated into larger data analysis platforms or used as a standalone tool for ethnic diversity analysis.
27. The system of claim 12 wherein the multiple ethnicities of the first layers does not include at least one of the first ethnicity or second ethnicity.
Electronics and semiconductor manufacturing. This technology addresses issues related to the precise deposition of multiple material layers in the fabrication of electronic components. Specifically, it concerns a system for creating a layered structure where the ethnicities, or compositions, of successive material layers are controlled to achieve desired device properties. The system involves a process for depositing a first set of layers and then a second set of layers. The critical aspect is that the composition of these layers is carefully selected. In a particular embodiment, the multiple ethnicities used for the first set of layers are deliberately chosen such that they exclude at least one of a first specific ethnicity and a second specific ethnicity. This selective exclusion allows for fine-tuning of the interface properties, stress distribution, or other characteristics between different material stacks within the device, enabling improved performance or novel functionalities.
28. The system of claim 12 wherein the first set of training data comprising first breast images do not comprise the third images or fourth images.
This invention relates to a medical imaging system for breast cancer detection, specifically addressing the challenge of training machine learning models with diverse and representative datasets. The system includes a training module that processes a first set of breast images, which are used to train a machine learning model to detect abnormalities such as tumors or lesions. The first set of images excludes a second set of images, which may include third and fourth images that could introduce bias or noise into the training process. The exclusion ensures that the model is trained on a clean, unbiased dataset, improving its accuracy and reliability. The system also includes a validation module that evaluates the trained model using a separate set of breast images to assess its performance. The validation process helps identify any overfitting or underfitting issues, ensuring the model generalizes well to new, unseen data. The system may further include a user interface for radiologists to review and annotate images, providing feedback that can be used to refine the model. The overall goal is to enhance the diagnostic accuracy of breast cancer detection by leveraging a well-curated training dataset and robust validation techniques.
29. The system of claim 12 wherein the first breast images comprises mammograms.
The system is designed for breast imaging analysis, specifically using mammograms to detect and evaluate abnormalities in breast tissue. Mammograms are X-ray images of the breast used for early detection of breast cancer and other conditions. The system processes these images to identify and analyze features that may indicate the presence of tumors, calcifications, or other anomalies. The imaging system includes components for capturing high-resolution mammographic images, storing the data, and applying image processing algorithms to enhance visibility and accuracy of detected features. The system may also incorporate machine learning or artificial intelligence to improve detection rates and reduce false positives. By automating the analysis of mammograms, the system aims to assist radiologists in diagnosing breast conditions more efficiently and accurately, reducing the need for invasive follow-up procedures. The technology addresses the challenges of manual interpretation, which can be time-consuming and prone to human error, particularly in detecting subtle or early-stage abnormalities. The system's ability to handle mammographic data makes it suitable for integration into existing radiology workflows, enhancing diagnostic capabilities in clinical settings.
30. The system of claim 12 wherein the first set of training data comprises ethnicity data for a general population, the second set of training data comprises ethnicity data for a Hispanic ethnicity, the third set of training data comprises ethnicity data for a Chinese ethnicity, and the fourth set of training data comprises ethnicity data for an Indian ethnicity.
This invention relates to a system for analyzing and processing ethnicity data to improve the accuracy of demographic classification. The system addresses the problem of biased or inaccurate demographic predictions in machine learning models by incorporating diverse training datasets. The system includes a data processing module that receives multiple sets of training data, each representing different ethnic groups. Specifically, the system uses a first dataset containing ethnicity data for a general population, a second dataset for Hispanic ethnicity, a third dataset for Chinese ethnicity, and a fourth dataset for Indian ethnicity. These datasets are used to train a classification model, ensuring that the model accounts for variations across these ethnic groups. The system may also include a validation module to assess the model's performance and an output module to provide refined demographic predictions. By leveraging these distinct datasets, the system enhances the accuracy and fairness of ethnicity-based classifications in applications such as healthcare, marketing, or social research. The invention aims to mitigate biases that arise from relying on a single, homogeneous dataset, thereby improving the reliability of demographic analysis.
31. The system of claim 16 wherein each of the first layers, second layer, and third layer has a single vote, and a diagnosis probability of breast cancer is based on a majority of the votes such that any two of the first layers, second layer, and third layer would constitute a majority.
This invention relates to a diagnostic system for breast cancer detection using a multi-layered voting mechanism. The system addresses the challenge of improving diagnostic accuracy by reducing reliance on a single decision-making layer, which can be prone to errors due to variability in data interpretation or model biases. The system includes three distinct layers: a first layer, a second layer, and a third layer. Each layer independently evaluates input data to generate a diagnostic vote. The first layer may process raw imaging or clinical data using machine learning models, while the second and third layers could apply different algorithms or feature sets to ensure diversity in analysis. Each layer outputs a binary vote (e.g., "cancer" or "no cancer") based on its evaluation. The final diagnosis probability is determined by a majority vote among the three layers. Importantly, the system is designed such that any two of the three layers can form a majority, meaning the diagnosis is confirmed if at least two layers agree. This redundancy ensures robustness, as a single layer's error does not override the consensus of the other two. The system may also incorporate confidence thresholds or additional validation steps to refine the final output. This approach enhances reliability by leveraging multiple independent assessments, reducing false positives and negatives in breast cancer diagnosis.
32. The system of claim 16 wherein the multiple ethnicities of the first layers include the first and second ethnicities.
The system relates to a multi-layered facial recognition or analysis system designed to improve accuracy across diverse ethnic groups. The core problem addressed is the bias and reduced accuracy in facial recognition systems when applied to individuals from different ethnic backgrounds. Traditional systems often perform poorly on certain ethnic groups due to training data imbalances or algorithmic biases. The system includes multiple layers of facial recognition models, each optimized for different ethnic groups. The first layers specifically handle the first and second ethnicities, ensuring that these groups are accurately processed. Additional layers may extend to other ethnicities, creating a hierarchical structure where early layers focus on the most critical or underrepresented groups. This layered approach allows the system to prioritize accuracy for specific ethnicities while maintaining overall performance. The system dynamically selects the appropriate layers based on input data, ensuring that the most relevant models are applied to each individual. This adaptive mechanism improves recognition accuracy and reduces bias by tailoring the processing pipeline to the user's ethnicity. The system may also include feedback mechanisms to continuously improve model performance across all ethnic groups. The overall goal is to create a more equitable and accurate facial recognition system that performs consistently across diverse populations.
33. The system of claim 16 wherein the multiple ethnicities of the first layers does not include at least one of the first ethnicity or second ethnicity.
This invention relates to a system for analyzing or processing data involving multiple ethnicities, particularly in layered data structures. The system addresses the challenge of ensuring diversity and representation in datasets by selectively excluding certain ethnicities from specific layers while maintaining others. The core system includes multiple layers of data, where each layer is associated with one or more ethnicities. The system allows for the exclusion of at least one predefined ethnicity (first or second ethnicity) from the first layers, ensuring that these layers do not include data from those excluded groups. This selective exclusion can be used to prevent bias, improve fairness, or meet regulatory requirements in applications such as machine learning, demographic analysis, or personalized services. The system may also include mechanisms to track, validate, or adjust the ethnicity associations across layers to maintain consistency and accuracy. The exclusion feature ensures that the system can dynamically adapt to different use cases where certain ethnicities need to be omitted from initial processing stages while still allowing their inclusion in subsequent layers if required. This approach enhances flexibility in handling sensitive demographic data while promoting ethical and unbiased outcomes.
34. The system of claim 16 wherein the first set of training data comprising first breast images do not comprise the third images or fourth images.
This invention relates to a medical imaging system for breast cancer detection, specifically addressing the challenge of improving diagnostic accuracy by training machine learning models on distinct sets of breast images. The system includes a training module that processes multiple sets of breast images to train a machine learning model. The first set of training data consists of breast images that do not include certain third or fourth images, ensuring the model is trained on a specific subset of data to avoid bias or contamination. The system also includes a classification module that uses the trained model to analyze new breast images and generate diagnostic outputs, such as identifying potential tumors or abnormalities. The training module may employ techniques like data augmentation or normalization to enhance the model's performance. The classification module may integrate additional features, such as patient history or clinical data, to refine its predictions. The system aims to improve diagnostic consistency and reduce false positives by leveraging well-defined training datasets and advanced machine learning techniques.
35. The system of claim 16 wherein the first breast images comprises mammograms.
The system relates to medical imaging, specifically breast imaging for early detection and diagnosis of breast cancer. The problem addressed is the need for accurate and efficient analysis of breast images to improve diagnostic outcomes. The system includes a breast imaging device that captures multiple images of a patient's breasts from different angles or using different imaging modalities. These images are processed to enhance visibility of abnormalities, such as tumors or calcifications, and to reduce artifacts caused by tissue density or positioning. The system may also include a display for presenting the processed images to a radiologist or automated analysis software for further evaluation. In one configuration, the breast images are mammograms, which are X-ray images of the breast used to detect early signs of cancer. The system may incorporate techniques such as digital image processing, machine learning, or artificial intelligence to analyze the mammograms and identify suspicious areas that require further examination. The goal is to improve the accuracy and efficiency of breast cancer screening and diagnosis, reducing false positives and negatives while minimizing the need for invasive follow-up procedures. The system may also integrate with electronic health records to provide a comprehensive view of a patient's imaging history.
36. The system of claim 16 wherein the first set of training data comprises ethnicity data for a general population, the second set of training data comprises ethnicity data for a Hispanic ethnicity, the third set of training data comprises ethnicity data for a Chinese ethnicity, and the fourth set of training data comprises ethnicity data for an Indian ethnicity.
This invention relates to a system for analyzing and processing ethnicity data to improve accuracy in demographic classification. The system addresses the problem of biased or inaccurate demographic predictions by using multiple sets of training data representing different ethnic groups. The system includes a data processing module that receives input data, such as images or other demographic information, and applies machine learning models to classify the data into specific ethnic categories. The system uses four distinct training datasets: one for a general population, one for Hispanic ethnicity, one for Chinese ethnicity, and one for Indian ethnicity. Each dataset is tailored to enhance the system's ability to distinguish between these ethnic groups with higher precision. The system may also include a validation module to assess the accuracy of the classifications and adjust the models accordingly. By leveraging specialized training data for each ethnic group, the system aims to reduce errors in demographic analysis and improve fairness in classification tasks. The invention is particularly useful in applications requiring accurate ethnic identification, such as healthcare, marketing, and social research.
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July 15, 2021
April 2, 2024
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